Utilities typically manage peak energy demand by load control programs which limit equipment use during peak hours. In the residential and small commercial environments this is typically performed by a Load Control Switch (LCS), also known in the industry as a Load Control Receiver, which interrupts the control line from a thermostat to its associated heating ventilation and air conditioning (HVAC) unit or directly interrupts the power line feeding the appliance which could be not only an HVAC unit but other type of loads like water heaters, pool pumps, etc.
LCSs have normally a receiver (for one-way communications from the utility to the LCS) or a transceiver (for two way communications) using a variety of technologies either wired (like Power Line Carrier) to wireless (pager, cellular, ZigBee™, Wi-Fi™ and others). Therefore the utility has the capability to send Load Control commands to the LCS to limit the usage of the equipment connected to the LCS by setting a maximum duty cycle for a limited period of time (i.e. peak reduction hours).
However there are scenarios where some consumers may have oversized equipment for the space to be conditioned, i.e. a high capacity air conditioner which runs only 40 to 60% of the time even during the hottest days of the summer. In this case, when the utility needs to reduce the demand by 50%, a Load Control event that limits the equipment usage to 50% will hardly affect the households with high capacity equipment while penalizing those with smaller units. On the other hand, it will be harder for the utility to predict the amount of overall load reduction since a number of units will be still running as usual while others will be limited to the set 50%. This may force the utility to further reduce the allowed running time to perhaps 40 or 30 percent to achieve its target and with it heavily penalizing the households with smaller units.
A proposed solution to address this issue is to send a Load Control command which sets, instead of a duty cycle limit, a percentage of duty cycle reduction with respect to the predicted equipment usage during the hours of the Load Control event. This solution is known in the industry as Adaptive Cycling (other terms are also being used like Average Load Adjustment Percentage among others).
The success of this solution greatly depends on how accurate the usage prediction is.
A simple method is to just determine equipment usage during the hour(s) prior to the Load Control event and extrapolate this usage to the event hours. This has two disadvantages: It ignores the house thermal behavior as the day unfolds (i.e. the heat exchange rate due to indoor-outdoor temperature differences and solar radiation absorption, among other factors, changes hour by hour) and also does not account for occupants behavior (their schedules and setting preferences may not require space conditioning until later in the day so if equipment usage was minimal or null during the hours prior to the event, the extrapolation of this usage may prevent the equipment from running during the most demanding hours when the occupants usually need air conditioning).
Another method may keep historic data to learn about the average usage of the equipment but it does not take into account the environmental changes from day to day to properly estimate the usage for the particular day a Load Control event is issued by the utility (typically a hotter day than the ones in the history).
A third method may require network assistance by directing the LCS to just store historical data during particularly hot days with no Load Control event issued. This has the disadvantage of having too few days used as a baseline which may coincide with singularities of the household occupants' behavior thus not reflecting a typical historic usage. Another disadvantage is that special daily commands must be issued to indicate whether the day qualifies as a baseline equipment usage. A third disadvantage is that by using as a baseline the hottest days of the summer, it will be impossible to estimate the usage for a milder day which may still require a Load Control event to offset an increase of demand from other consumers (industrial, etc.) or compensate for the temporary loss of generation or distribution capacity.
The method disclosed addresses all the above mentioned limitations to compute a more accurate prediction of equipment usage without using any external environmental sensors (indoor/outdoor temperature sensors) and/or special network commands, as it also learns about the typical household occupants' behavior without access to thermostat set points and schedule.
Accordingly, systems and methods that enable adaptive load control remains highly desirable.
It will be noted that throughout the appended drawings, like features are identified by like reference numerals.